Abstract and Introduction
Abstract
Background: Acute kidney injury (AKI) is a common and severe complication of cardiac surgery with cardiopulmonary bypass (CPB). This study aimed to establish a model to predict the probability of postoperative AKI in patients undergoing cardiac surgery with CPB.
Methods: We conducted a retrospective, multicenter study to analyze 1082 patients undergoing cardiac surgery under CPB. The least absolute shrinkage and selection operator regression model was used to optimize feature selection for the AKI model. Multivariable logistic regression analysis was applied to build a prediction model incorporating the feature selected in the previously mentioned model. Finally, we used multiple methods to evaluate the accuracy and clinical applicability of the model.
Results: Age, gender, hypertension, CPB duration, intraoperative 5% bicarbonate solution and red blood cell transfusion, urine volume were identified as important factors. Then, these risk factors were created into nomogram to predict the incidence of AKI after cardiac surgery under CPB.
Conclusion: We developed a nomogram to predict the incidence of AKI after cardiac surgery. This model can be used as a reference tool for evaluating early medical intervention to prevent postoperative AKI.
Introduction
Acute kidney injury (AKI) is a common and severe complication of cardiac surgery with CPB.[1,2] The apparent characteristics of AKI are rapid development, poor prognosis, high mortality, and complicated pathogenesis.[3–5] Studies have shown that as many as 42% of patients undergoing CPB heart surgery have cardiac surgery-related AKI after surgery, of which 2–6% of patients require continuous renal replacement therapy (CRRT), and the associated survival time is reduced, which usually bring a heavy burden to society. At present, there are few effective treatments for CPB-related AKI in the world, mainly including conservative medication and CRRT as supportive measures.[6,7] And clinically, the diagnosis of AKI is mainly based on serum creatinine (Scr) and urea nitrogen levels, but these indicators are not sensitive to the early prediction of kidney damage.[8,9] Furthermore, an emerging method in recent years is to detect biomarkers such as NGAL, KIM-1, miRNA-21 in blood or urine for early diagnosis of AKI. Whereas, these biomarkers are usually only a few hours earlier than the detection of Scr, and some biomarkers are also present in other diseases and cause false positives.[10] Thereby, it is necessary to identify high-risk patients early and give appropriate postoperative AKI support.
In order to rationally manage AKI after cardiac surgery, a model that can accurately predict high-risk patients to optimize postoperative AKI treatment strategies is very urgent. The Cleveland University Acute Renal Failure Score System (Cleveland Score),[11] Simplified Kidney Index Score (SRI Score) and other researchers have established predictive models of AKI after cardiac surgery, which are widely used.[12] However, these models are mainly based on old clinical data collected more than ten years ago, and mainly come from western populations. For clinicians, early prediction, early diagnosis and management of AKI after cardiac surgery are still challenging. Hence, the establishment of risk models for such patients still has important clinical significance.
The nomogram transforms the complex regression equation into a visual graph, making the results of the prediction model easier to read and more convenient to evaluate the patient's condition. Due to its intuitive and easy-to-understand characteristics, nomogram has gradually received more and more attention and applications in medical research and clinical practice.[13,14]
However, nomograms are rarely used in AKI after cardiac surgery. Therefore, this study intends to use perioperative clinical information to include some preoperative and intraoperative risk factors that may affect the occurrence of postoperative AKI into the study. According to the AKI standard proposed by KDIGO in 2012, a nomogram prediction model was established through multivariate logistic regression analysis. Predict the prevalence of AKI in patients undergoing CPB cardiac surgery, and provide clinical guidance for early identification and intervention of high-risk patients.[15]
BMC Anesthesiol. 2022;22(379) © 2022 BioMed Central, Ltd.
